{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[3] Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run the following from the cs231n directory and try again:\n",
      "python setup.py build_ext --inplace\n",
      "You may also need to restart your iPython kernel\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_val:  (1000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch normalization: Forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:  [ 27.18502186  34.21455511  37.68611762]\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  mean:  [ -8.88178420e-18   1.05471187e-16   1.85268467e-17]\n",
      "  std:  [ 0.99999999  1.          1.        ]\n",
      "After batch normalization (nontrivial gamma, beta)\n",
      "  means:  [ 11.  12.  13.]\n",
      "  stds:  [ 0.99999999  1.99999999  2.99999999]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization\n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print('  means: ', a.mean(axis=0))\n",
    "print('  stds: ', a.std(axis=0))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'})\n",
    "print('  mean: ', a_norm.mean(axis=0))\n",
    "print('  std: ', a_norm.std(axis=0))\n",
    "\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print('After batch normalization (nontrivial gamma, beta)')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:  [ 1.01531428  1.01238373  0.97819988]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch Normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.7029238452e-09\n",
      "dgamma error:  7.42041421625e-13\n",
      "dbeta error:  2.87950576558e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch Normalization: alternative backward (OPTIONAL, +3 points extra credit)\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For the sigmoid function, it turns out that you can derive a very simple formula for the backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can also derive a simple expression for the batch normalization backward pass if you work out derivatives on paper and simplify. After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster.\n",
    "\n",
    "NOTE: This part of the assignment is entirely optional, but we will reward 3 points of extra credit if you can complete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  1.06890440382e-12\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 0.05x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs2312n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the flag `use_batchnorm` is `True` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.26119551013\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 2.85e-06\n",
      "W3 relative error: 3.92e-10\n",
      "b1 relative error: 2.22e-03\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 4.78e-11\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.99653322011\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.28e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 5.55e-09\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 2.64e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            use_batchnorm=True)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 2.340974\n",
      "(Epoch 0 / 10) train acc: 0.101000; val_acc: 0.112000\n",
      "(Epoch 1 / 10) train acc: 0.244000; val_acc: 0.211000\n",
      "(Epoch 2 / 10) train acc: 0.345000; val_acc: 0.282000\n",
      "(Epoch 3 / 10) train acc: 0.404000; val_acc: 0.294000\n",
      "(Epoch 4 / 10) train acc: 0.438000; val_acc: 0.302000\n",
      "(Epoch 5 / 10) train acc: 0.485000; val_acc: 0.299000\n",
      "(Epoch 6 / 10) train acc: 0.537000; val_acc: 0.315000\n",
      "(Epoch 7 / 10) train acc: 0.598000; val_acc: 0.317000\n",
      "(Epoch 8 / 10) train acc: 0.605000; val_acc: 0.325000\n",
      "(Epoch 9 / 10) train acc: 0.663000; val_acc: 0.341000\n",
      "(Epoch 10 / 10) train acc: 0.727000; val_acc: 0.338000\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.155000; val_acc: 0.140000\n",
      "(Epoch 1 / 10) train acc: 0.182000; val_acc: 0.160000\n",
      "(Epoch 2 / 10) train acc: 0.213000; val_acc: 0.178000\n",
      "(Epoch 3 / 10) train acc: 0.237000; val_acc: 0.229000\n",
      "(Epoch 4 / 10) train acc: 0.263000; val_acc: 0.238000\n",
      "(Epoch 5 / 10) train acc: 0.301000; val_acc: 0.257000\n",
      "(Epoch 6 / 10) train acc: 0.327000; val_acc: 0.295000\n",
      "(Epoch 7 / 10) train acc: 0.354000; val_acc: 0.245000\n",
      "(Epoch 8 / 10) train acc: 0.416000; val_acc: 0.289000\n",
      "(Epoch 9 / 10) train acc: 0.408000; val_acc: 0.288000\n",
      "(Epoch 10 / 10) train acc: 0.447000; val_acc: 0.285000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PHsyhhx7Kv/71LwB+/OMfM2zYMG6++WY2bNjAz372M1wuF47j8Mknn/CDH/yAF198kQcf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\nDsvmrZjH2++/DUD3tt0pzC3kmyd/k8LcQk7ocwIdW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94dUdpGa820q6mBLVsaryj75BP7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\nqSUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x493f7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o', label='baseline')\n",
    "plt.plot(bn_solver.loss_history, 'o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.train_acc_history, '-o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(solver.val_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.val_acc_history, '-o', label='batchnorm')\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers = {}\n",
    "solvers = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dsbfdaUlveZ9h7IOflVJTMUalK4nR9O5ls5nVdIxhsceYF1EbMWoZWwBhWuvFWusrSqmf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\nHVr+matnmHdgHnMPzGXj8Y24urjyuP/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FzUY3IyGQwLxu81SAiXgUiQUYaHqsHL5Ro2Dx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EoRqRlUJng42d3Adbjk5mKgFtCXvEPOhgE/A+2Bu4D7gxKP3wHilWngPc9PuKFsjwB3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\nLwoNukZMRHze+XAI1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YjGGGOMMSe4aM4+6Fc/OzN3smrbKp5e/zR/+b+/cM3iazh/1vkk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L445EonbQQXDkkTBgAKxaFXc0uaGoCLbbDjbdNO5I6q+UVkc0s8eS3zpwv5n9VO7hBsAewOup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mU1CTcmZLnNsp+WpcWIFhyOP8zBuIMk+hIzzFMxrCI1K9Y8N5yVzzq0VS9fPU/P0oZ6k0lr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\nUggTERERERFx0f8D6czqaMHhglYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x49387b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(10, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Question:\n",
    "Describe the results of this experiment, and try to give a reason why the experiment gave the results that it did."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Answer:\n",
    "The result show that with batch normalization model could be:\n",
    "much more robust to bad initialization(even with a bad initialization can get good performance)\n",
    "much more robust to avoid overfitting（one some scale without bn the model the a bigger gap between val and train）\n",
    "prevent vanishing gradient and gradient explosion problem\n",
    "I think the reason is that batch normalization prevent the input values of the layers to be too small or too big, so during backpropagation the network can get a better gradient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
